Diagnosis of Mango Leaf Diseases Using Deep Learning Techniques
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Abstract
Mango cultivation is a cornerstone of Thailand's agriculture and economy, but diseases such as anthracnose, algal leaf spot, and gall midge present significant challenges as they can reduce crop yield and quality. In this study, we developed a machine learning-based system to diagnose mango leaf diseases using a dataset of 1,900 images collected from mango orchards in Phitsanulok province. The data underwent preprocessing and augmentation to optimize model training. Five deep learning models—Convolutional Neural Network (CNN), VGG16, DenseNet121, ResNet50, and InceptionV3—were trained and evaluated. Among these, ResNet50 demonstrated the best performance, with an accuracy of 99.8%, a precision of 0.998, a recall of 0.998, and an F1-score of 0.998. Leveraging its superior performance, the ResNet50 model was integrated into a mobile application designed for real-time disease diagnosis. This user-friendly application enables mango farmers to upload images of affected leaves and receive instant disease identification and treatment recommendations. The findings highlight the potential of deep learning models in agricultural applications, offering a reliable and efficient tool for early disease detection and management. By enabling timely intervention, this innovation enhances crop health, reduces losses, and boosts productivity, contributing significantly to sustainable farming practices and improving farmers' livelihoods.
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References
Ahmed, M., Afreen, N., Ahmed, M., Sameer, M., & Ahamed, J. (2023). An inception V3 approach for malware classification using machine learning and transfer learning. International Journal of Intelligent Networks, 4, 11-18.
Amiruddin, B. P., & Kadir, R. E. A. (2020). CNN architectures performance evaluation for image classification of mosquito in Indonesia. In 2020 International seminar on intelligent technology and its applications (ISITIA) (pp. 223-227). IEEE. https://doi.org/10.1109/ISITIA49792.2020.9163732
Arulananth, T., Prakash, S. W., Ayyasamy, R. K., Kavitha, V., Kuppusamy, P., & Chinnasamy, P. (2024). Classification of paediatric pneumonia using modified DenseNet-121 deep-learning model. IEEE Access, 12, 35716-35727.
Brahmi, W., Jdey, I., & Drira, F. (2024). Exploring the role of convolutional neural networks (CNN) in dental radiography segmentation: A comprehensive systematic literature review. Engineering Applications of Artificial Intelligence, 133(Part E), Article 108510. https://doi.org/10.1016/j.engappai.2024.108510
Chowdary, G. J., Punn, N. S., Sonbhadra, S. K., & Agarwal, S. (2020). Face mask detection using transfer learning of inception v3. Big data analytics: 8th international conference (pp. 81-90). Springer-Verlag. https://doi.org/10.1007/978-3-030-66665-1_6
Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311-318.
Hossain, M. B., Iqbal, S. M. H. S., Islam, M. M., Akhtar, M. N., & Sarker, I. H. (2022). Transfer learning with fine-tuned deep CNN ResNet50 model for classifying COVID-19 from chest X-ray images. Informatics in Medicine Unlocked, 30, Article 100916. https://doi.org/10.1016/j.imu.2022.100916
Huang, X., Duan, Z., Hao, S., Hou, J., Chen, W., & Cai, L. (2025). A deep learning framework for corrosion assessment of steel structures using Inception v3 model. Buildings, 15(4), Article 512. https://doi.org/10.3390/buildings15040512
Kattenborn, T., Leitloff, J., Schiefer, F., & Hinz, S. (2021). Review on Convolutional neural networks (CNN) in vegetation remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 173, 24-49.
Khalifa, N. E., Wang, W., Mawgoud, A. A., & Zhang, Y.-D. (2025). COECG-resnet-GWO-SVM: an optimized COVID-19 electrocardiography classification model based on resnet50, grey wolf optimization and support vector machine. Multimedia Tools and Applications, 84(17), 18305-18325.
Koonce, B., (2021). ResNet 50. In B. Koonce (Ed.). Convolutional neural networks with swift for tensorflow: image recognition and dataset categorization (pp. 63-72). Apress. https://doi.org/10.1007/978-1-4842-6168-2_6
Kumar, P., Ashtekar, S., Jayakrishna, S., Bharath, K., Vanathi, P., & Kumar, M. R. (2021). Classification of mango leaves infected by fungal disease anthracnose using deep learning. In 2021 5th international conference on computing methodologies and communication (ICCMC) (pp. 1723-1729). IEEE. https://doi.org/10.1109/ICCMC51019.2021.9418383
Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2021). A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Transactions on Neural Networks and Learning Systems, 33(12), 6999-7019.
Mascarenhas, S., & Agarwal, M. (2021). A comparison between VGG16, VGG19 and ResNet50 architecture frameworks for image classification. In 2021 international conference on disruptive technologies for multi-disciplinary research and applications (CENTCON) (pp. 96-99). IEEE. https://doi.org/10.1109/CENTCON52345.2021.9687944
Misra, A. K. (1992). Important diseases of mango and their effect on production. Biological Memoirs, 18(1&2), 39-55.
Mukesh, C., Likhita, A., & Yamini, A. (2024). Performance analysis of InceptionV3, VGG16, and Resnet50 models for crevices recognition on surfaces. In S. J. Nanda, R. P. Yadav, A. H. Gandomi, & M. Saraswat (Eds.). Data science and applications (pp. 161-172). Springer. https://doi.org/10.1007/978-981-99-7817-5_13
Nandhini, S., & Ashokkumar, K. (2022). An automatic plant leaf disease identification using DenseNet-121 architecture with a mutation-based henry gas solubility optimization algorithm. Neural Computing and Applications, 34(7), 5513-5534.
O'Shea, K. (2015). An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458.
Pandian, J. A., Kumar, V. D., Geman, O., Hnatiuc, M., Arif, M., & Kanchanadevi, K. (2022). Plant disease detection using deep convolutional neural network. Applied Sciences, 12(14), Article 6982. https://doi.org/10.3390/app12146982
Rao, U. S., Swathi, R., Sanjana, V., Arpitha, L., Chandrasekhar, K., Chinmayi, & Naik, P. K. (2021). Deep learning precision farming: grapes and mango leaf disease detection by transfer learning. Global Transitions Proceedings, 2(2), 535-544. https://doi.org/10.1016/j.gltp.2021.08.002
Rochmawanti, O., & Utaminingrum, F. (2021). Chest X-ray image to classify lung diseases in different resolution size using DenseNet-121 architectures. In Proceedings of the 6th international conference on sustainable information engineering and technology (pp. 327-331). Association for Computing Machinery. https://doi.org/10.1145/3479645.3479667
Saleem, M. H., Potgieter, J., & Arif, K. M. (2019). Plant disease detection and classification by deep learning. Plants, 8(11), Article 468. https://doi.org/10.3390/plants8110468
Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1), Article 60. https://doi.org/10.1186/s40537-019-0197-0
Simangunsong, P. B. N., Sihombing, P., Efendi, S., & Fahmi, F. (2024). Densenet-121 analysis for classification of tomato leaf diseases. In 2024 7th international seminar on research of information technology and intelligent systems (ISRITI) (pp. 907-911). IEEE. https://doi.org/10.1109/ISRITI64779.2024.10963618
Sujitranan, M., & Anongporn, S. (2024). Exploring deep learning features and bag-of-visual-words for scene classification. ICIC Express Letters, Part B: Applications, 15(10), 1081-1088. https://doi.org/10.24507/icicelb.15.10.1081
Taylor, B., Marco, V. S., Wolff, W., Elkhatib, Y., & Wang, Z. (2018). Adaptive deep learning model selection on embedded systems. ACM Sigplan Notices, 53(6), 31-43.
Theckedath, D., & Sedamkar, R. (2020). Detecting affect states using VGG16, ResNet50 and SE-ResNet50 networks. SN Computer Science, 1(2), Article 79. https://doi.org/10.1007/s42979-020-0114-9
Xia, X., Xu, C., & Nan, B. (2017). Inception-v3 for flower classification. In 2017 2nd international conference on image, vision and computing (ICIVC) (pp. 783-787). https://doi.org/10.1109/ICIVC.2017.7984661.IEEE
Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018). Convolutional neural networks: an overview and application in radiology. Insights into Imaging, 9, 611-629.
Yang, L., Xu, S., Yu, X., Long, H., Zhang, H., & Zhu, Y. (2023). A new model based on improved VGG16 for corn weed identification. Frontiers in Plant Science, 14, Article 1205151. https://doi.org/10.3389/fpls.2023.1205151
